Trajectory Design for Multi-UAV Aided Communication with Actor-critic-based Reinforcement Learning

• 1、School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876

Abstract：In this paper, the trajectory design problem is investigated in wireless communications aided by multiple unmanned aerial vehicles (UAVs), and a multi-UAV trajectory design method called multi-agent twin delayed deep deterministic policy gradient (MA-TD3) is proposed which is able to design continuous trajectories without pre-knowledge of global information such as user locations and channel conditions, through integrating the multi-agent deep deterministic policy gradient (MADDPG) algorithm and twin delayed deep deterministic policy gradient (TD3) algorithm based on actor-critic reinforcement learning (RL) framework. In particular, the multi-UAV trajectory design problem is firstly formulated as a stochastic game (SG) to maximize the completion rate of the transmission tasks. Then, the MA-TD3 method is proposed which is based on the actor-critic RL framework and the learned trajectory is obtained successively. Numerical results show that compared to traditional single agent RL methods, the proposed MA-TD3 method achieves higher completion rate of the transmission tasks by enabling cooperation between multiple UAVs through centralized training and distributed execution.

keywords： Communication and Information System trajectory design multi-UAV aided communication multi-agent reinforcement learning

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